Keras中CNN联合LSTM进行分类

def get_model():
    n_classes = 6
    inp=Input(shape=(40, 80))
    reshape=Reshape((1,40,80))(inp)
 #   pre=ZeroPadding2D(padding=(1, 1))(reshape)
    # 1
    conv1=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(reshape)
    #model.add(Activation('relu'))
    l1=LeakyReLU(alpha=0.33)(conv1)

    conv2=ZeroPadding2D(padding=(1, 1))(l1)
    conv2=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(conv2)
    #model.add(Activation('relu'))
    l2=LeakyReLU(alpha=0.33)(conv2)

    m2=MaxPooling2D((3, 3), strides=(3, 3))(l2)
    d2=Dropout(0.25)(m2)
    # 2
    conv3=ZeroPadding2D(padding=(1, 1))(d2)
    conv3=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv3)
    #model.add(Activation('relu'))
    l3=LeakyReLU(alpha=0.33)(conv3)

    conv4=ZeroPadding2D(padding=(1, 1))(l3)
    conv4=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv4)
    #model.add(Activation('relu'))
    l4=LeakyReLU(alpha=0.33)(conv4)

    m4=MaxPooling2D((3, 3), strides=(3, 3))(l4)
    d4=Dropout(0.25)(m4)
    # 3
    conv5=ZeroPadding2D(padding=(1, 1))(d4)
    conv5=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv5)
    #model.add(Activation('relu'))
    l5=LeakyReLU(alpha=0.33)(conv5)

    conv6=ZeroPadding2D(padding=(1, 1))(l5)
    conv6=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv6)
    #model.add(Activation('relu'))
    l6=LeakyReLU(alpha=0.33)(conv6)

    m6=MaxPooling2D((3, 3), strides=(3, 3))(l6)
    d6=Dropout(0.25)(m6)
    # 4
    conv7=ZeroPadding2D(padding=(1, 1))(d6)
    conv7=Convolution2D(256, 3, 3, border_mode='same',init='glorot_uniform')(conv7)
    #model.add(Activation('relu'))
    l7=LeakyReLU(alpha=0.33)(conv7)

    conv8=ZeroPadding2D(padding=(1, 1))(l7)
    conv8=Convolution2D(256, 3, 3, border_mode='same',init='glorot_uniform')(conv8)
    #model.add(Activation('relu'))
    l8=LeakyReLU(alpha=0.33)(conv8)
    g=GlobalMaxPooling2D()(l8)
    print("g=",g)
    #g1=Flatten()(g)
    lstm1=LSTM(
        input_shape=(40,80),
        output_dim=256,
        activation='tanh',
        return_sequences=False)(inp)
    dl1=Dropout(0.3)(lstm1)
    
    den1=Dense(200,activation="relu")(dl1)
    #model.add(Activation('relu'))
    #l11=LeakyReLU(alpha=0.33)(d11)
    dl2=Dropout(0.3)(den1)

#     lstm2=LSTM(
#         256,activation='tanh',
#         return_sequences=False)(lstm1)
#     dl2=Dropout(0.5)(lstm2)
    print("dl2=",dl1)
    g2=concatenate([g2,dl2],axis=1)
    d10=Dense(1024)(g2)
    #model.add(Activation('relu'))
    l10=LeakyReLU(alpha=0.33)(d10)
    l10=Dropout(0.5)(l10)
    l11=Dense(n_classes, activation='softmax')(l10)



    model=Model(input=inp,outputs=l11)
    model.summary()
    #编译model
    adam = keras.optimizers.Adam(lr = 0.0005, beta_1=0.95, beta_2=0.999,epsilon=1e-08)
    #adam = keras.optimizers.Adam(lr = 0.001, beta_1=0.95, beta_2=0.999,epsilon=1e-08)
    #sgd = keras.optimizers.SGD(lr = 0.001, decay = 1e-06, momentum = 0.9, nesterov = False)

    #reduce_lr = ReduceLROnPlateau(monitor = 'loss', factor = 0.1, patience = 2,verbose = 1, min_lr = 0.00000001, mode = 'min')
    model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])

    
    return model